Coding policy

When coding responses, missing items in this study were coded as “9”, and responses such as “don’t know” were coded as “99”. Hair et al. (1998) stated that “missing data under 10% for an individual case or observation can generally be ignored”. Thus, the amount of missing data was low enough (under 10%) to avoid affecting the results, even if it operated in a non-random manner (Hair et al., 1998). Thus, missing data in this study could be ignored (see Appendices 6.1 and 6.2). In this study, means calculated from the valid responses of adopters and non-adopters were assumed to be applied in the case of missing data and “don't know” items, so

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no potential biases existed in the patterns of missing data (Hair et al. 2010, 53; Malhotra 2010). Overall, the combined missing data and “don’t know” responses were between zero and 6% for adopters, and between zero and 9% for non-adopters.

“Outliers” are observations that distinctly differ from other observations in the data. They were taken as a relatively insignificant portion in this case, since only seven (2.7%) outliers were detected, with 257 respondents of adopters, and no outliers detected with 174 respondents of non-adopters. Thus, they should be of little concern when there are only a few outliers within a large sample size (Kline 1994).

A number of assumptions about the data had to be tested before the analysis could be allowed to proceed:

1) Linearity of the phenomenon screened in a scatterplot;

2) Constant variance of the error terms tested in homoscedasticity of variance; 3) Independence of the error terms examined by Durbin-Watson;

4) Normality of the error term distribution screened by normal probability plots and diagnosed by z kurtosis, z skewness, and Kolmogorov-Smirnov test (Hair et al. 1998).

Firstly, results from the scatterplot between the dependent and independent variables did not indicate any nonlinear relationships in this study. The Pearson’s correlation was also calculated for the linearity of the relationship between dependent and independent variables. The results are presented later in this chapter.

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Secondly, the Levene test, showing homoscedasticity of variance, was performed. With minimal violations of this assumption found, there was no corrective action needed (Hair et al. 2010, 208) (Appendices 6.3 and 6.4).

Thirdly, the independence of the error terms was checked to see if the predicted value (independent variables) was related to any other prediction (dependent variables). Because the samples were cross-sectional data in this study, rather than consisting of time-series data, the independence of the error terms has shown no violation of this assumption.

Finally, the results of distribution normality were checked. Kurtosis and skewness of the distribution were calculated via a Kolmogorov-Smirnov test. The tests showed that some constructs were not normally distributed (with p < 0.05). Hair et al. (2010, 78) recommended that “data transformations can provide the principal means of correcting non-normality. [...] In many instances, the researcher may apply all of the possible transformations”. This study has attempted to employ some possible transformations, which included the square root, logarithms, squared, cubed, and inverse, in order to gain the best-transformed results. After attempting these means of transformation, the results indicated that the squared root was the only and best means of transformation, and that only four constructs (i.e. adopter’s “usability and relevance”, non-adopter’s “subjective norm”, “operational concerns”, and “normative influence”) could be improved after the normality transformations were performed (Tabachnick and Fidell 2007, 98-99). The distribution properties before and after the transformation are shown in Tables 6.4 and 6.5.

164 Table 6.4: Normality test – adopters

N=257

Before After

Skewness Kurtosis Kolmogorov-Smirnov Transfor-_mation Kolmogorov -Smirnov

Sig. Sig. Intention -0.938 0.696 0.000 a 0.000 Attitude -0.767 2.641 0.000 a 0.000 Subjective norm -1.447 2.676 0.000 a 0.000 Perceived behavioural control -1.427 2.400 0.000 a 0.000 Usability and relevance -1.053 1.502 0.000 Squared root 0.200 Operational concerns -1.419 2.407 0.017 a 0.000 Innovative- ness -0.830 1.333 0.008 a 0.000 Normative influece 0.265 -0.696 0.003 a 0.000 Self-efficacy -0.484 -0.166 0.005 a 0.000 Facilitating condition -0.451 -0.124 0.000 a 0.000

Note a: The transformation paths tried included square root, logarithms, squared, cubed, and inverse.

165 Table 6.5: Normality test – non-adopters

N=174

Before After

Skewness Kurtosis Kolmogorov-Smirnov Transform_ation Kolmogorov-Smirnov

Sig. Sig. Intention 0.357 -0.256 0.200 --- Attitude 0.155 0.077 0.083 --- Subjective norm -0.457 0.224 0.001 Squared root 0.156 Perceived behavioural control 0.123 -0.119 0.052 --- Usability and relevance -0.077 -0.908 0.200 --- Operational concerns -0.714 -0.287 0.000 Squared root 0.015 Innovative- ness -0.104 -0.154 0.200 --- Normative influence 0.687 0.076 0.001 Squared root 0.200 Self-efficacy 0.139 -0.036 0.200 --- Facilitating condition 0.133 0.185 0.094 ---

Although the results seemed to indicate a non-normal distribution for some constructs, the literature suggested some exceptions that would enable us to handle such violations. Curran, West, and Finch (1996, 26) found “significant problems

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arising with univariate skewness of 2.0 and kurtoses of 7.0”. In this study, normality problems were not serious, because all of the constructs’ skewness coefficients were less than 2.0, while their kurtoses were less than 7.0.

In addition, Greene (2008, 18) mentioned that “normality is not necessary to obtain any of the results we use in multiple regression analysis, although it will enable us to obtain several exact statistical results”. Pallant (2007, 204) also mentioned that “most of the techniques are reasonably “robust” or tolerant of violations of this assumption”. Bryman and Cramer (2001) and Micceri (1989) found that some variables in social science studies do show a non-normal distribution. Bryman and Cramer (1997, 96) stated that researchers often have to treat variables as if they are normally distributed.

Finally, in order to avoid the difficulties with the interpretation of final results after a transformation of the data, this study will stay with the original data without transformation, because the normality problems of this study were not particularly serious.